Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Artificial Intelligence in Finance ACCFIN5230

  • Academic Session: 2022-23
  • School: Adam Smith Business School
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

The course provides an introduction to the main artificial intelligence (AI) algorithms and present its applications in Finance.


Course is delivered over 2 weeks, comprising of 14 hours of lectures and 2 hours of tutorials.

Requirements of Entry

Registration on the MSc Financial Technology programme

Excluded Courses





ILO being assessed

Course Aims

The overall aim of the course is to introduce the main algorithms of AI and inform the students about is applications in Finance. The course aims to introduce Neural Networks, evolutionary programming, meta-heuristics and deep learning to students. The advantages and disadvantages of AI in Finance will be discussed along with its applications through a series of case studies and research papers. 

Intended Learning Outcomes of Course

By the end of this course students will be able to:


1.   Understand, explain and critically assess the main Neural Networks algorithms.

2.   Understand, explain and critically assess the main evolutionary programming algorithms.

3.   Understand and critically assess the applications of Neural Networks, evolutionary programming, meta-heuristics in Finance.

4.   Understand and critically assess the concept of deep learning in Finance applications

Minimum Requirement for Award of Credits

Students must submit at least 100% by weight of the components of the course's summative assessment.